Dataset Information

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

ABSTRACT: Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person's movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person's and beacons' localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant's localization error was 1.05 ± 0.44 m, and the average beacons' localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons' data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).

SUBMITTER: Ceron JD

PROVIDER: S-EPMC7506668 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

Ceron Jesus D JD Kluge Felix F Küderle Arne A Eskofier Bjoern M BM López Diego M DM

Sensors (Basel, Switzerland) 20200822 17

Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person's movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person's and beacons' localization is performed simultaneously using a combination o ...[more]

PMID: 32842566

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Project description:BackgroundThe number of fatal pedestrian injuries in the United States has steadily increased over the past decade. Multiple factors likely contribute to this trend, but the growth of pedestrians distracted by mobile devices is widely hypothesized to play a major role. Existing strategies to reduce distracted pedestrian behavior are few and mostly ineffective. The present study evaluated StreetBit, a mostly-passive primary prevention program to reduce distracted pedestrian behavior by alerting distracted pedestrians directly on their smartphone when they approach an intersection, reminding them to attend to traffic as they crossed.Methods385 individuals who regularly crossed a target street corner at an urban university downloaded StreetBit on their phones and participated in a crossover design study whereby the app was inactive for 3 weeks (baseline behavior phase), actively provided alerts for 3 weeks (intervention phase), and then was inactive again for 4 weeks (post-intervention phase). User distraction while crossing the intersection was collected electronically for a total of 34,923 street-crossing events throughout the 10-week study.ResultsIn crude (unadjusted) models, participant distraction was similar across all phases of the research; this result was maintained after adjusting for potential covariates as well as after conducting a sensitivity analysis limited to data from only week 3 of each study intervention phase. In a model stratified by phone/warning type and baseline distraction rates, Android phone users who received a warning that blocked the full screen and had a high baseline distraction rate (≥75% distracted crossings) had a 64% decreased odds of distraction during the alert phase (OR 0.36, 95% CI 0.25-0.51) and a 52% decreased odds of distraction during the post-intervention phase (OR 0.48, 95% CI 0.25-0.94). Users reported positive impressions about the StreetBit app in a post-intervention survey.DiscussionStreetBit, an innovative app designed to prevent distracted pedestrian behavior through a mostly-passive primary prevention strategy relying on intrusive reminders, proved effective among smartphone users who received a warning blocking the full screen and who were frequently distracted at baseline, but not among other users. The results appear to reflect the confluence of two influencing factors. First, due to software development limitations, visually-distracted Android users received a highly intrusive app warning that blocked their smartphone screen whereas iOS users received a less intrusive banner notification blocking a small upper portion of the screen. Second, most users were curious to see if the app was functioning properly, creating artificially-inflated estimates of distraction as users purposefully watched their phones when crossing. Thus, our results indicate promise for StreetBit as an effective intervention and warrant continued software development and empirical testing.

Project description:ObjectiveOver 6400 American pedestrians die annually, a figure that is currently increasing. One hypothesised reason for the increasing trend is the role of mobile technology in distracting both pedestrians and drivers. Scientists and policy-makers have attended somewhat to distracted driving, but attention to distracted pedestrian behaviour has lagged. We will evaluate Bluetooth beacon technology as a means to alert and warn pedestrians when they approach intersections, reminding them to attend to the traffic environment and cross streets safely.MethodsBluetooth beacons are small devices that broadcast information unidirectionally within a closed proximal network. We will place beacons at an intersection frequently trafficked by urban college students. From there, the beacons will transmit to an app installed on users' smartphones, signalling users to attend to their environment and cross the street safely. A cross-over trial will evaluate the app with 411 adults who frequently cross the target intersection on an urban university campus. We will monitor those participants' behaviour over three distinct time periods: (1) 3 weeks without the app being activated, (2) 3 weeks with the app activated and (3) 4 weeks without the app activated to assess retention of behaviour. Throughout the 10-week period, we will gather information to evaluate whether the intervention changes distracted pedestrian behaviour using a logistic regression to estimate the likelihood of key behavioural outcome measures and adjusting for any residual confounding. We also will test for changes in perceived risk. The trial will follow CONSORT (Consolidated Standards of Reporting Trials) statement guidelines, as modified for cross-over design studies.ConclusionIf this program proves successful, it offers exciting implications for future testing and ultimately for broad distribution to reduce distracted pedestrian behavior. We discuss issues of feasibility, acceptability and scalability.

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Dataset Information

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

Publications

Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data.

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